Sanjaya Lall Memorial Lecture 2024

01:13:23
https://www.youtube.com/watch?v=tkIhHbc2IAE

Summary

TLDRÎn această prelegere, profesorul Darren Acemoglu abordează subiectul provocărilor și oportunităților generate de noile tehnologii, precum inteligența artificială (AI), asupra economiei și societății. El se concentrează pe două aspecte esențiale: rolul relațiilor de putere și natura tehnologică a automatizării în determinarea impactului economic al inovațiilor. Prin exemple istorice, precum Revoluția Industrială Britanică și evoluția fabricilor auto din secolul XX, Acemoglu ilustrează modul în care tehnologiile noi au generat atât progres economic, cât și provocări legate de distribuția inechitabilă a veniturilor. Importanța instituțiilor democratice și a inovării direcționate responsabil este subliniată ca fiind esențială pentru a asigura că beneficiile tehnologiilor sunt împărțite echitabil. Profesorul avertizează asupra consecințelor unei automatisări necontrolate și consideră că o participare mai activă a societății civile și reglementări adecvate sunt necesare pentru a naviga cu succes era digitală.

Takeaways

  • 🤖 Tehnologia poate crește eficiența producției, dar nu garantează distribuția echitabilă a prosperității.
  • ⚖️ Relațiile de putere influențează modul în care beneficiile tehnologice sunt distribuite în societate.
  • 📉 Automatizarea excesivă poate reduce valoarea marginală a muncii și salariile reale.
  • ⚙️ Istoria arată că inovațiile nu au condus întotdeauna la creșterea nivelului de trai pentru toți.
  • 🔄 Nevoia de instituții democratice și sindicalizare pentru un echilibru economic echitabil.
  • 💡 Tehnologiile pot fi utilizate pentru a îmbunătăți productivitatea umană dacă sunt direcționate corect.
  • 🏭 Exemplele istorice arată cum tehnologia poate perpetua inegalitatea dacă nu este gestionată corect.
  • 💬 AI și digitalizare: posibile atât pentru automatizare, cât și pentru îmbunătățirea abilităților umane.
  • 🌍 Impactul global al AI și cum poate schimba diviziunea muncii între țări.
  • 📊 Importanța reglementărilor și a societății civile în orientarea progresului tehnologic.

Timeline

  • 00:00:00 - 00:05:00

    Evenimentul este deschis cu un discurs de bun venit și introducerea lui Darren Asoglu, un renumit profesor de economie la MIT și vizitator la Oxford. Amfitrionul Andrew Steven oferă un context despre conferința memorială Sanaya La și contribuțiile notabile ale profesorului Zan Al.

  • 00:05:00 - 00:10:00

    Darren își prezintă lucrarea din cartea sa, concentrându-se pe impactul tehnologiilor asupra societății și economiei. Subiectul central este cine beneficiază și controlează noile tehnologii, în special în contextul modificărilor tehnologice recente din AI.

  • 00:10:00 - 00:15:00

    El discută despre optimismul tehnologic și prezumția economică conform căreia tehnologiile noi, deși disruptivante, aduc beneficii generale. Introduce conceptul de „productivity bandwagon”, care sugerează că noile tehnologii ar trebui să ducă la creșteri salariale reale.

  • 00:15:00 - 00:20:00

    Darren argumentează că această presupunere este simplificată și adesea ignoră aspecte precum cine controlează tehnologia și cum afectează distribuția veniturilor. Folosește exemple istorice pentru a ilustra că productivitatea crescută nu duce întotdeauna la creșteri ale cererii de muncă.

  • 00:20:00 - 00:25:00

    Indian exemplifică două tehnologii transformative: morile de vânt și ginul de bumbac al lui Eli Whitney. Deși au revoluționat economiile lor, muncitorii nu au beneficiat echitabil, mai ales din cauza forței coercitive și a relațiilor de putere.

  • 00:25:00 - 00:30:00

    Revoluția industrială britanică este analizată pentru a demonstra că creșterea productivității nu a beneficiat muncitorii în primele faze, iar coerciția și automatizarea au jucat un rol important în acest sens.

  • 00:30:00 - 00:35:00

    Analiza continuă cu dezvoltarea tehnologică modernă și impactul asupra salariilor reale în SUA, subliniind distrugerea partajării prosperității observată după 1980 și creșterea inegalității în multe economii industrializate.

  • 00:35:00 - 00:40:00

    Se folosesc exemple din industria auto, unde inițial inovațiile tehnologice au creat locuri de muncă și partajarea prosperității prin sindicate, dar ulterior direcția s-a schimbat spre automatizare, reducând cererea de muncă consolidată cu puterea muncii.

  • 00:40:00 - 00:45:00

    Discuția se îndreaptă spre influența gândirii ideologice și a priorităților afacerilor, menționând influența lui Milton Friedman și schimbarea accentului către reducerea costurilor și profit pentru acționari, în detrimentul împărțirii câștigurilor.

  • 00:45:00 - 00:50:00

    Subliniază că beneficiarii tehnologiei sunt determinate de modul în care alegem să dezvoltăm și să utilizăm tehnologiile, având în vedere relațiile de putere și dacă inovațiile conduc la automatizare sau la sarcini noi pentru muncitori.

  • 00:50:00 - 00:55:00

    Prezintă diferite perspective tehnologice în istoria computerelor, de la vizunea lui Alan Turing către utilitatea tehnologiei pentru oameni discutată de Norbert Wiener, reflectând asupra impactului AI, fie ca un instrument de control, fie ca o oportunitate de îmbunătățire a capacităților umane.

  • 00:55:00 - 01:00:00

    Disputează polaritatea dintre automare și control, subliniind importanța deciziilor democratice și ale mișcărilor societății civile în determinarea direcției în care se dezvoltă tehnologiile digitale.

  • 01:00:00 - 01:05:00

    În arrațiunea despre energia regenerabilă demonstrează cum presiunea societală și reglementările au avut un impact asupra reducerii costurilor tehnologiilor verzi, sugerând cum ar putea avea loc un impact similar în AI.

  • 01:05:00 - 01:13:23

    În final, discuțiile de Q&A ating teme precum concentrarea pieței, impactul AI asupra țărilor cu acces limitat la tehnologie și potențialul de redistribuire a beneficiilor economice, întărind ideea că alegerea noastră colectivă va defini viitorul.

Show more

Mind Map

Mind Map

Frequently Asked Question

  • Cine este Darren Acemoglu?

    Darren Acemoglu este profesor la MIT și este renumit pentru cercetările sale în domeniul economiei.

  • Care este subiectul principal al prelegerii?

    Subiectul principal este impactul tehnologiei asupra economiei și cum poate influența distribuția veniturilor.

  • Ce teme abordează profesorul în prelegere?

    Profesorul discută despre relațiile de putere, automatizare și modul în care tehnologia afectează piața muncii.

  • Există exemple istorice discutate în prelegere?

    Da, sunt discutate Revoluția Industrială Britanică și alte inovații tehnologice semnificative din istorie.

  • Ce poziție are profesorul față de automatizare?

    Acemoglu este precaut față de automatizare, subliniind importanța analizării cum și cine controlează noile tehnologii.

  • Cum afectează tehnologia distribuția veniturilor, potrivit profesorului?

    Noul val de tehnologii poate crește inegalitatea veniturilor dacă relațiile de putere nu sunt corecte.

  • Ce soluții propune pentru o distribuție echitabilă a prosperității tehnologice?

    Propune schimbări instituționale și democratice pentru a asigura ca beneficiile tehnologiei să fie distribuite mai echitabil.

  • Cum vede rolul AI în economie?

    AI poate fi un instrument de control și automatizare, dar are și potențialul de a îmbunătăți productivitatea umană dacă este utilizată corect.

  • Există o soluție pentru adaptarea la noile tehnologii?

    Este necesară o implicare mai puternică a societății civile și reglementări guvernamentale eficiente.

  • Ce exemple moderne de impact al tehnologiei sunt discutate?

    Se discută despre impactul AI și al altor tehnologii digitale asupra locurilor de muncă contemporane.

View more video summaries

Get instant access to free YouTube video summaries powered by AI!
Subtitles
en
Auto Scroll:
  • 00:00:08
    everybody Welcome to uh the Sanaya La
  • 00:00:11
    Memorial lecture with Professor Darren
  • 00:00:13
    asoglu um I'm Professor Andrew Steven
  • 00:00:16
    I'm the deputy Dean for faculty research
  • 00:00:18
    here at the Sai business school and it's
  • 00:00:21
    an absolute pleasure to be uh hosting uh
  • 00:00:24
    Darren uh in his time um as the uh
  • 00:00:28
    visiting Professor the San well visiting
  • 00:00:30
    Professor uh here at Oxford this uh this
  • 00:00:33
    month um so I'm not going to take up too
  • 00:00:35
    much time because we want to hear from
  • 00:00:37
    Darren and then we'll have a Q&A uh with
  • 00:00:39
    the audience um but as I'm sure you're
  • 00:00:43
    all well aware Darren is a uh a very
  • 00:00:46
    very very highly accomplished
  • 00:00:48
    world-renowned um academic in the field
  • 00:00:51
    of Economics uh he is going to share
  • 00:00:55
    some work from his book uh with us this
  • 00:00:58
    evening uh and then we'll have a discuss
  • 00:01:00
    around that Darren is the Institute
  • 00:01:02
    professor at the department of Economics
  • 00:01:03
    at MIT and the sanay visiting Professor
  • 00:01:06
    here in Oxford um and we're just as I
  • 00:01:09
    said delighted to have him here just a
  • 00:01:11
    little bit of background for those of
  • 00:01:12
    you who aren't familiar with uh this
  • 00:01:15
    visiting professorship um which has been
  • 00:01:17
    running for um about a decade um the
  • 00:01:20
    sanal visiting professorship um here in
  • 00:01:23
    oxid was created to honor the memory and
  • 00:01:25
    academic Legacy of Professor Sanel this
  • 00:01:28
    distinguished scholarship ship scheme
  • 00:01:30
    has run for actually as I said over a
  • 00:01:32
    decade and it's invited some of the most
  • 00:01:34
    prestigious International economists
  • 00:01:36
    here to Oxford um with with Darren being
  • 00:01:39
    the most recent um Honore uh under this
  • 00:01:43
    scheme um for those of you who uh don't
  • 00:01:46
    know U much about Professor Zan Al just
  • 00:01:48
    a little a bit about him and his his
  • 00:01:51
    career here as a development Economist
  • 00:01:53
    and professor of Economics here at the
  • 00:01:55
    University of Oxford uh his research
  • 00:01:57
    interests included the impact of foreign
  • 00:01:59
    direct invest ment in developing
  • 00:02:01
    countries the E the economics of
  • 00:02:02
    multinational corporations and the
  • 00:02:04
    development of technological capability
  • 00:02:07
    and Industrial competitiveness in
  • 00:02:08
    developing countries he's one of the
  • 00:02:10
    world's preeminent development
  • 00:02:12
    economists L was also one of the
  • 00:02:14
    founding editors of the Oxford
  • 00:02:16
    development studies journal and also a
  • 00:02:19
    senior Economist at the World Bank and
  • 00:02:20
    so it's a real pleasure that we can
  • 00:02:22
    honor his legacy with this visiting
  • 00:02:25
    professorship um that uh we're we're
  • 00:02:28
    honoring tonight with uh Professor
  • 00:02:30
    Darren Asam MoGo so without further Ado
  • 00:02:32
    I'm going to ask Darren to come on up to
  • 00:02:34
    the elcon and um give us our talk you
  • 00:02:37
    saw the QR code there on the screen um
  • 00:02:40
    actually if we can just quickly bring
  • 00:02:42
    that back um in the Q&A part this
  • 00:02:46
    evening we'll have mics roaming around
  • 00:02:48
    in the lecture theater but if you're
  • 00:02:50
    watching online on the live stream or
  • 00:02:53
    you'd rather not hold a microphone and
  • 00:02:55
    speak but just put something uh online
  • 00:02:57
    then use that uh that QR code which will
  • 00:03:00
    take you to a web page where you can
  • 00:03:01
    submit your questions and then I'll
  • 00:03:03
    moderate the discussion uh with Darren
  • 00:03:05
    and see the questions on an iPad uh when
  • 00:03:08
    the time comes all right Darren over to
  • 00:03:11
    you thank you thank you very much Andrew
  • 00:03:13
    and uh uh it's my true pleasure to be
  • 00:03:15
    here thank you to the S business school
  • 00:03:18
    for hosting me and thank you to the L
  • 00:03:20
    family for uh their great Hospitality as
  • 00:03:22
    well and thank you to all of you for
  • 00:03:24
    being here and it's my pleasure to be at
  • 00:03:27
    Oxford it's also my pleasure to share
  • 00:03:29
    share uh some thoughts from my recent
  • 00:03:32
    book with Simon Johnson power and PR
  • 00:03:34
    progress our Thousand-Year struggle over
  • 00:03:37
    technology and prosperity I think the
  • 00:03:39
    title essentially frames the question
  • 00:03:41
    that I want to talk about today which is
  • 00:03:43
    that we are in the middle of some pretty
  • 00:03:47
    uh impressive changes in
  • 00:03:51
    technology from biotech to communication
  • 00:03:55
    Technologies and especially most
  • 00:03:56
    recently to breakthroughs in AI there is
  • 00:04:00
    tremendous amount of
  • 00:04:03
    excitement
  • 00:04:06
    but often under
  • 00:04:10
    appreciated is how Society adapts and
  • 00:04:15
    works with new technologies in
  • 00:04:17
    particular in regards to the question of
  • 00:04:19
    who will benefit from new technologies
  • 00:04:21
    who will control these new technologies
  • 00:04:23
    the emphasis on
  • 00:04:26
    struggle is for a reason because we
  • 00:04:31
    think this is one of the most important
  • 00:04:34
    axes of understanding the impact of
  • 00:04:36
    technology over society and economy
  • 00:04:38
    especially during this transformative
  • 00:04:40
    age and it's not receiving its due
  • 00:04:44
    attention why are we perhaps
  • 00:04:49
    not worrying as much about who controls
  • 00:04:52
    technology and what technology does
  • 00:04:54
    especially in terms of income
  • 00:04:56
    distribution winners and losers and I
  • 00:04:58
    think there are two sorts of reasons
  • 00:05:02
    especially looked at it from the other
  • 00:05:05
    side of the Atlantic where you see
  • 00:05:07
    techno optimism in the media in policy
  • 00:05:10
    circles has been very very strong that
  • 00:05:12
    somehow we will work out the rough edges
  • 00:05:15
    of the adjustment process to new
  • 00:05:17
    technology and the second is actually on
  • 00:05:20
    both sides of the Atlantic is related to
  • 00:05:22
    the economic science that there is a
  • 00:05:25
    widespread belief in economics that
  • 00:05:29
    while there are disruptive effects of
  • 00:05:32
    Technologies there is a very
  • 00:05:36
    powerful
  • 00:05:37
    mechanism at work which will ultimately
  • 00:05:41
    make sure that we will generally tend to
  • 00:05:45
    benefit all of us will generally tend to
  • 00:05:47
    benefit from new
  • 00:05:49
    technologies and of course since most of
  • 00:05:52
    us actually earn our livings in the
  • 00:05:54
    labor market that mechanism has to go
  • 00:05:56
    through the labor
  • 00:05:58
    market s and I call it the productivity
  • 00:06:00
    bandwagon it is a very very widespread
  • 00:06:03
    idea in
  • 00:06:05
    economics perhaps so widespread that it
  • 00:06:07
    doesn't have a name so we had to
  • 00:06:08
    Christen it what does the productivity
  • 00:06:10
    bone bandwagon says essentially the uh
  • 00:06:13
    argument is very
  • 00:06:14
    simple at least superficially and I'm
  • 00:06:17
    going to argue that it actually gains
  • 00:06:20
    its Simplicity from perhaps ignoring
  • 00:06:23
    some important considerations product
  • 00:06:25
    technology improves so our knowledge
  • 00:06:27
    improves so for example we didn't know
  • 00:06:28
    how to do
  • 00:06:30
    uh large language models before now we
  • 00:06:32
    do if we use that the right way that's
  • 00:06:35
    an that's an if there are Technologies
  • 00:06:37
    in the past we haven't used it for
  • 00:06:39
    improving productivity but if we use it
  • 00:06:41
    the not the way that we use nuclear
  • 00:06:43
    weapons or not the way that we used uh
  • 00:06:45
    other things which uh have been deployed
  • 00:06:48
    for destructive reasons and in the case
  • 00:06:50
    of AI I think there are some questions
  • 00:06:52
    but it's not a crazy presumption then
  • 00:06:54
    that's going to improve
  • 00:06:56
    productivity meaning we're going to be
  • 00:06:58
    able to produce more goods and services
  • 00:07:00
    or higher quality goods and services
  • 00:07:03
    with the same amount of inputs in
  • 00:07:04
    particular with the same amount of Labor
  • 00:07:07
    and then the presumption of the
  • 00:07:08
    productivity bandwagon is
  • 00:07:11
    that that will somehow translate to
  • 00:07:15
    higher real
  • 00:07:17
    wages meaning that now workers are going
  • 00:07:19
    to earn more and are going to have the
  • 00:07:23
    capacity to purchase more goods and
  • 00:07:25
    services so we've now spread all these
  • 00:07:28
    gains more widely in society so why
  • 00:07:30
    would that be the case so if I write it
  • 00:07:32
    this way as I've done here on the left
  • 00:07:35
    it seems like reasonable but you know if
  • 00:07:38
    we dig into it why would it be the
  • 00:07:40
    case so the reasoning
  • 00:07:43
    goes something like this and it's a
  • 00:07:45
    little more subtle than perhaps that
  • 00:07:48
    chart on the left might first
  • 00:07:50
    suggest the idea is that productivity
  • 00:07:54
    increases that makes employers want to
  • 00:07:58
    hire more labor because now labor has
  • 00:08:01
    become more productive the given amount
  • 00:08:02
    of Labor can produce more goods and
  • 00:08:05
    services and if employers rush out
  • 00:08:10
    to demand more
  • 00:08:13
    labor the market process the competitive
  • 00:08:16
    process is going to bid up
  • 00:08:18
    wages now if once you start spelling it
  • 00:08:21
    out this way you'll realize that there
  • 00:08:24
    are two presumptions
  • 00:08:27
    here one
  • 00:08:29
    which I'm going to spend quite a bit of
  • 00:08:30
    time on is that somehow an increase in
  • 00:08:34
    productivity makes workers sorry makes
  • 00:08:37
    firms want to hire more labor I'm going
  • 00:08:40
    to actually question that I'm going to
  • 00:08:42
    question that because it's actually a
  • 00:08:44
    fairly non-trivial step and we don't
  • 00:08:47
    want to be lulled into a presumption
  • 00:08:49
    that's not really true and sounds true
  • 00:08:52
    so we're we're going to dig dig into
  • 00:08:54
    that but I'm going to start from the
  • 00:08:56
    second presumption here on this slide
  • 00:08:58
    and then come to that that one in a
  • 00:09:00
    second which actually is a very
  • 00:09:02
    important
  • 00:09:03
    one that the market process works
  • 00:09:06
    without regard
  • 00:09:09
    to the topics that other social sciences
  • 00:09:12
    worry a lot about for example power who
  • 00:09:14
    has power who has coercive capacity who
  • 00:09:16
    has the ability to impose things on them
  • 00:09:19
    so it may it is a non-trivial
  • 00:09:24
    step that if employers want more labor
  • 00:09:27
    that will immediately autom atically
  • 00:09:30
    without any barriers translate into
  • 00:09:32
    higher real wages and therefore the
  • 00:09:34
    shared Prosperity step will be completed
  • 00:09:36
    well my argument is going to be
  • 00:09:38
    throughout this talk that throughout
  • 00:09:41
    history and this history is really
  • 00:09:43
    important here I'll come back for the
  • 00:09:44
    reasons the reasons why throughout
  • 00:09:46
    history there are instances where you
  • 00:09:48
    see the productivity bandwagon at work
  • 00:09:50
    so it's not a crazy presumption but
  • 00:09:53
    there are also instances where it hasn't
  • 00:09:56
    worked and it hasn't worked for exact
  • 00:09:59
    these two steps that I have just
  • 00:10:01
    outlined breaking down either because
  • 00:10:05
    power related
  • 00:10:07
    considerations have been important
  • 00:10:13
    right
  • 00:10:15
    or because productivity increases don't
  • 00:10:17
    necessarily translate employers wanting
  • 00:10:19
    more labor so let me start with the
  • 00:10:21
    latter so here is an illustration of two
  • 00:10:25
    very transformative Technologies okay
  • 00:10:26
    I'm not going to be in the business of
  • 00:10:28
    uh you you know what Silicon Valley Tech
  • 00:10:30
    leaders do it's like AI is more
  • 00:10:32
    important than fire I'm not going to say
  • 00:10:34
    AI is more important or less important
  • 00:10:35
    but these are pretty important new
  • 00:10:36
    technologies the one on the left is the
  • 00:10:40
    perhaps the most transformative
  • 00:10:42
    breakthrough of the Middle Ages where
  • 00:10:45
    you know the Dark Ages so-called because
  • 00:10:47
    of cultural reasons were not dark
  • 00:10:49
    technologically there were many many
  • 00:10:50
    many Innovations in the production
  • 00:10:52
    process that really transformed
  • 00:10:53
    agricultural production but none was as
  • 00:10:55
    important as this thing which is
  • 00:10:57
    windmills that really tremendously
  • 00:11:00
    increased the ability of people to use
  • 00:11:02
    energy for a variety of
  • 00:11:05
    tasks the right one the the one on the
  • 00:11:08
    right hand side is equally important is
  • 00:11:10
    Eli Whitney's cotton gin which enable
  • 00:11:12
    the type of cotton that could be grown
  • 00:11:14
    in the US South to be cleaned both of
  • 00:11:17
    these Technologies were truly
  • 00:11:19
    revolutionary in terms of their economic
  • 00:11:21
    consequences they really changed the
  • 00:11:22
    organization of production in many parts
  • 00:11:24
    the windmill in much of medieval Europe
  • 00:11:28
    the uh uh Eli Whitney's cut engin or
  • 00:11:31
    actually Eli Whitney was one of the
  • 00:11:32
    people who invented the cut engine there
  • 00:11:34
    were several others but Eli Whitney gets
  • 00:11:36
    the credit uh the the cut engine
  • 00:11:39
    completely transformed the US southern
  • 00:11:41
    economy it was a complete Backwater
  • 00:11:44
    economically it became the largest
  • 00:11:45
    exporter of cotton in the world and uh
  • 00:11:49
    engine of the British Industrial
  • 00:11:51
    Revolution which was mostly about cotton
  • 00:11:53
    textile in the early phases in both
  • 00:11:57
    cases and you want f much of a
  • 00:11:59
    productivity bandwagon the medieval
  • 00:12:01
    period despite the series of Innovations
  • 00:12:03
    including the windmill did not see much
  • 00:12:06
    improvements in the real living
  • 00:12:07
    standards of the workers who were the
  • 00:12:10
    farmers and if you think about that for
  • 00:12:13
    a few minutes you'll realize why that's
  • 00:12:17
    quite expected actually because the much
  • 00:12:20
    of the medieval economy was in a
  • 00:12:23
    coercive environment workers were often
  • 00:12:25
    in survi relations or even if when they
  • 00:12:28
    were not in survi Rel
  • 00:12:30
    they had a lot of obligations to uh uh
  • 00:12:35
    uh uh big land owners and and and other
  • 00:12:38
    powerful actors such as the uh uh the
  • 00:12:41
    church
  • 00:12:42
    hierarchy and when more labor was
  • 00:12:47
    demanded those obligations could be
  • 00:12:49
    increased via the coercive power of the
  • 00:12:53
    high aristocracy and the church
  • 00:12:55
    hierarchy and the evidence seems to
  • 00:12:58
    suggest that activity increased a lot
  • 00:13:00
    but a lot of it was captured by a very
  • 00:13:02
    small fraction of the population about
  • 00:13:04
    5% or so who went around building
  • 00:13:06
    wonderful monuments such as the ones you
  • 00:13:08
    see in Oxford but people remained
  • 00:13:11
    poor the one on the right is even
  • 00:13:13
    clearer to see who were the workers they
  • 00:13:16
    were the black enslaved workers and of
  • 00:13:20
    course when sudden plantation owners
  • 00:13:24
    decided oh my God now there is a great
  • 00:13:26
    Economic Opportunity in the form of
  • 00:13:28
    cotton Plantation they didn't tell the
  • 00:13:31
    slaves oh well how much more money
  • 00:13:32
    should we give you they told them okay
  • 00:13:35
    fine you're moving to the Mississippi
  • 00:13:36
    Delta the down deep south where
  • 00:13:38
    conditions were much worse long hours uh
  • 00:13:41
    much more coercive relations and uh and
  • 00:13:45
    living standards of the slaves actually
  • 00:13:47
    worsened now you might think these two
  • 00:13:51
    examples
  • 00:13:53
    are
  • 00:13:56
    non-representative because precisely
  • 00:13:59
    they are non-industrial Technologies
  • 00:14:01
    although the cotton genin is sort of
  • 00:14:03
    semi-industrial or it's adjacent to
  • 00:14:06
    Industrial and precisely because I have
  • 00:14:10
    emphasized coercion but coercion is just
  • 00:14:13
    the tip of a broader Iceberg which is
  • 00:14:15
    power power relations are as important
  • 00:14:18
    in every relation every production
  • 00:14:20
    relation not just in the medieval
  • 00:14:22
    economy but perhaps even more
  • 00:14:25
    quintessential for understanding
  • 00:14:27
    technology is the Industrial Revolution
  • 00:14:29
    ution after all the reason why Simon
  • 00:14:33
    Johnson and I put so much
  • 00:14:37
    emphasis in the book on history is
  • 00:14:42
    because my experience I think Simon's
  • 00:14:44
    experience also is that for the last 15
  • 00:14:47
    years when I talked about robots or
  • 00:14:50
    digital Technologies or other things
  • 00:14:53
    creating huge inequality wage declines
  • 00:14:56
    uh and and being misused
  • 00:14:59
    you would always get especially from
  • 00:15:01
    journalists in the US and tech people oh
  • 00:15:04
    you must be saying this time is
  • 00:15:05
    different because we know in history
  • 00:15:07
    everything has worked out look at the
  • 00:15:08
    British Industrial Revolution well I
  • 00:15:10
    don't know what British Industrial
  • 00:15:12
    Revolution they were looking at because
  • 00:15:13
    the history is actually very different
  • 00:15:15
    the history of the British Industrial
  • 00:15:16
    Revolution
  • 00:15:17
    is
  • 00:15:19
    not devoid of
  • 00:15:22
    coercion the factory system was a
  • 00:15:24
    coercive system at least in the early
  • 00:15:27
    phases but
  • 00:15:29
    most importantly it shows great
  • 00:15:32
    parallels to the other two examples that
  • 00:15:34
    I showed earlier on the British industri
  • 00:15:36
    revolution for all practical purposes
  • 00:15:38
    started sometime around 1740 1750 when
  • 00:15:43
    Advanced Machinery started being applied
  • 00:15:45
    developed and applied in uh in the
  • 00:15:46
    production process and the evidence is
  • 00:15:49
    that by and large we don't see much
  • 00:15:51
    improvement in the real earnings
  • 00:15:54
    inflation adjusted price adjusted
  • 00:15:56
    earnings of the working class for about
  • 00:15:58
    9 years up to about 1840 or so and the
  • 00:16:02
    most dynamic sectors of the economy for
  • 00:16:04
    example weaving that text the Cottons
  • 00:16:08
    that were being or uh cotton yarn that
  • 00:16:11
    was being exported or cotton that was
  • 00:16:14
    being exported and turned into yarn by
  • 00:16:15
    Spinners and then Weavers were
  • 00:16:17
    processing that they actually during the
  • 00:16:20
    process of the early industrialization
  • 00:16:22
    saw their real earnings decline by about
  • 00:16:26
    half moreover the
  • 00:16:31
    factory was indeed partly about control
  • 00:16:35
    and as a result the factory was a very
  • 00:16:37
    oppressive place and the working hours
  • 00:16:40
    of typical British worker increased by
  • 00:16:43
    about 20% so on an hourly basis real
  • 00:16:48
    wages probably declin for about a good
  • 00:16:50
    90 years so the sort of productivity
  • 00:16:54
    bandwagon saving us all you know for AI
  • 00:16:57
    just wait for another two years well if
  • 00:16:59
    the British Industrial Revolution is the
  • 00:17:01
    example we have to learn from doesn't
  • 00:17:03
    look so
  • 00:17:04
    good so what happened in the British
  • 00:17:07
    Industrial Revolution well again I
  • 00:17:09
    mentioned it's the power issue was very
  • 00:17:12
    important that's why it's in the title
  • 00:17:13
    of our book but there's another aspect
  • 00:17:16
    British industrial revolution's early
  • 00:17:19
    Technologies were all centered on
  • 00:17:22
    automation meaning substitution of
  • 00:17:25
    machinery for tasks previously performed
  • 00:17:28
    by workers
  • 00:17:31
    and what's so interesting about
  • 00:17:32
    automation is that
  • 00:17:35
    a this is a lot of what we're doing with
  • 00:17:38
    digital Technologies today and at least
  • 00:17:40
    one path of what we might be doing with
  • 00:17:44
    AI and
  • 00:17:46
    B it actually if you think about it
  • 00:17:49
    starts
  • 00:17:50
    questioning that other part of the
  • 00:17:53
    causal chain that productivity Rises and
  • 00:17:56
    then as productivity Rises employers
  • 00:17:58
    would like to hire more labor in fact
  • 00:18:02
    the reason why hen
  • 00:18:04
    Weavers did so well in terms of their
  • 00:18:07
    they were the labor aristocracy because
  • 00:18:08
    there was great demand for weaving
  • 00:18:11
    skills and why did their real incomes
  • 00:18:14
    decline
  • 00:18:15
    by by by declined by half well because
  • 00:18:19
    once weaving was mechanized
  • 00:18:21
    automated their skills were not
  • 00:18:24
    necessary so actually the big step here
  • 00:18:28
    doesn't follow so if I wanted to put it
  • 00:18:31
    in a slightly more wonkish terms when
  • 00:18:33
    people say productivity Rises and all
  • 00:18:36
    economists well many economists and all
  • 00:18:39
    journalists are guilty of this because
  • 00:18:41
    they're actually confusing two things
  • 00:18:44
    they're confusing average productivity
  • 00:18:46
    which is how much output is produced per
  • 00:18:48
    worker versus what economic theory tells
  • 00:18:51
    us what firm's value which is the
  • 00:18:54
    marginal productivity of workers meaning
  • 00:18:56
    what a worker contributes and why
  • 00:18:59
    these two Notions could go very
  • 00:19:01
    different ways well I think the best way
  • 00:19:03
    of understanding that is this is this
  • 00:19:05
    story which is supposed to be a humorous
  • 00:19:07
    story well unless you're a worker of
  • 00:19:08
    course uh which is
  • 00:19:11
    the it says the factory of the future
  • 00:19:14
    will have two employees a man and a dog
  • 00:19:17
    the man is there to feed the dog and the
  • 00:19:18
    dog is there to make sure that the man
  • 00:19:20
    doesn't touch the
  • 00:19:21
    equipment
  • 00:19:23
    so if indeed that is the future of the
  • 00:19:27
    factory well it's not very humorous for
  • 00:19:29
    the workers but it will have huge
  • 00:19:31
    average labor productivity you'll have
  • 00:19:33
    one worker or perhaps two if you count
  • 00:19:35
    the dog and a huge amount of output this
  • 00:19:37
    amazing Machinery is producing a
  • 00:19:40
    lot but the reason why this a humorous
  • 00:19:42
    story is that if the Machinery gets
  • 00:19:44
    better and you double output of course
  • 00:19:46
    firms are not going to rush out to hire
  • 00:19:47
    more men and their dogs the marginal
  • 00:19:50
    productivity the contribution of the
  • 00:19:52
    worker is
  • 00:19:53
    Trivial so if
  • 00:19:55
    automation is the order of the day
  • 00:19:59
    it takes tasks away from workers to give
  • 00:20:02
    it to Capital the reason why weaving
  • 00:20:04
    Machinery was so useful is because
  • 00:20:06
    Weavers were expensive they were the
  • 00:20:07
    labor
  • 00:20:08
    aristocracy you don't need the Weavers
  • 00:20:10
    anymore now Machinery does it that's
  • 00:20:13
    productive you produce more output but
  • 00:20:16
    it's just like the guy and his dog
  • 00:20:18
    they're not
  • 00:20:19
    needed so there are therefore two big
  • 00:20:24
    steps here that we have to think about
  • 00:20:26
    the productivity bandwagon in the past
  • 00:20:29
    and in the future in the age of
  • 00:20:32
    AI if we are going to hope that somehow
  • 00:20:35
    the market process the automatic process
  • 00:20:37
    is going to get us shared Prosperity it
  • 00:20:40
    must be through the productivity
  • 00:20:42
    badwagon
  • 00:20:43
    or some institutional responses I'll
  • 00:20:45
    come back to them and if it's going to
  • 00:20:46
    be the productivity bandwagon then we
  • 00:20:48
    need those two steps to work out somehow
  • 00:20:50
    new technologies will increase the
  • 00:20:52
    marginal productivity of Labor and power
  • 00:20:54
    issues are not going to make Capital
  • 00:20:56
    dominant over labor
  • 00:20:59
    these issues were of course Very Much
  • 00:21:02
    alive and very much understood during
  • 00:21:03
    the British Industrial Revolution Jeremy
  • 00:21:06
    benam came up with his
  • 00:21:08
    panopticon precisely arguing that this
  • 00:21:11
    was an efficiency enhancing technology
  • 00:21:13
    but many people understood right away
  • 00:21:16
    that this was actually a technology of
  • 00:21:17
    control it was a way of taking power
  • 00:21:21
    away from
  • 00:21:23
    workers and giving it more to Capital
  • 00:21:26
    and part of the reason why work fared
  • 00:21:29
    badly wasn't just working hours and low
  • 00:21:31
    incomes but because life expectancy fell
  • 00:21:33
    as low as 30 to 30 years at Birth in
  • 00:21:35
    cities like Manchester and London why
  • 00:21:38
    because they were shoved in into cities
  • 00:21:40
    with horrible working conditions and
  • 00:21:41
    nobody gave them public infrastructure
  • 00:21:43
    clean toilets or any sort of uh
  • 00:21:46
    amenities so these were all about
  • 00:21:49
    power power is going to be very
  • 00:21:51
    important in the future but this issue
  • 00:21:53
    of automation is going to be very
  • 00:21:54
    important as
  • 00:21:56
    well but of course when people tell you
  • 00:21:59
    and I'll tell you
  • 00:22:00
    that we are of course today in the year
  • 00:22:04
    2024 we are hugely fortunate that the
  • 00:22:07
    British Industrial Revolution happened
  • 00:22:09
    you know uh 200 years ago or start 250
  • 00:22:14
    years ago started uh we are much
  • 00:22:17
    healthier We Have Much Better Health
  • 00:22:19
    Technologies we have much better comfort
  • 00:22:21
    and we have we have much much much much
  • 00:22:22
    greater levels of real incomes so what
  • 00:22:25
    happened well things changed after 184
  • 00:22:28
    40 and how did they change they changed
  • 00:22:31
    because institutions
  • 00:22:35
    changed especially power relations with
  • 00:22:37
    democracy and trade unions in Britain
  • 00:22:40
    trade unions were very heavily
  • 00:22:41
    prosecuted up to the last quarter of the
  • 00:22:43
    19th century and the nature of
  • 00:22:46
    technology changed but let me actually
  • 00:22:49
    stop talking about the British
  • 00:22:50
    Industrial Revolution you could get
  • 00:22:52
    boring so let me talk about the modern
  • 00:22:55
    times let me talk about the modern times
  • 00:22:58
    and make the case that actually all of
  • 00:23:02
    these issues are not just hypothetical
  • 00:23:03
    theoretical things we have to sort of
  • 00:23:05
    think about for historical
  • 00:23:07
    Nuance this is real wages for 10
  • 00:23:11
    demographic groups in the United
  • 00:23:14
    States and it's for men and women men
  • 00:23:18
    here women here and different curves are
  • 00:23:21
    for different education groups workers
  • 00:23:23
    without a high school degree high school
  • 00:23:24
    graduates workers with associate degrees
  • 00:23:26
    just a college degree and those with
  • 00:23:27
    postgraduate degrees everything is
  • 00:23:30
    normalized to zero so you see the
  • 00:23:31
    cumulative real weage changes now these
  • 00:23:34
    figur shows two very contrasting period
  • 00:23:37
    of periods of uh economic growth in the
  • 00:23:40
    west or in the United States the other
  • 00:23:42
    countries are I'll show you a little bit
  • 00:23:44
    on that but this is the end of the
  • 00:23:48
    period of shared Prosperity that had
  • 00:23:49
    started know arguably in the 40s but uh
  • 00:23:55
    certainly you see it very strongly in
  • 00:23:56
    the 1950s but in this data set i'm
  • 00:23:58
    showing you from 1963 onwards you see
  • 00:24:00
    all these curves are actually on top of
  • 00:24:02
    each other you have real wage growth for
  • 00:24:05
    all of 10 of these demographic groups
  • 00:24:07
    are essentially parallel which means
  • 00:24:09
    their real wages are growing at the same
  • 00:24:10
    rate and they're growing very rapidly
  • 00:24:12
    how how rapid it's actually a
  • 00:24:13
    mindboggling 2 and a half% a year in
  • 00:24:16
    real terms that's faster than
  • 00:24:18
    productivity growth so labor was
  • 00:24:19
    benefiting from productivity growth
  • 00:24:21
    during this period as much as perhaps
  • 00:24:23
    slightly more than Capital that was the
  • 00:24:26
    center tenant of shared Prosperity so
  • 00:24:29
    you see the kind of process that in the
  • 00:24:32
    19th century in the early 20th century
  • 00:24:35
    was the basis of that period of shared
  • 00:24:38
    prosperity and you see it's oops and you
  • 00:24:40
    see it's undoing from around 1980 these
  • 00:24:43
    curves are going in different direction
  • 00:24:45
    but more striking is that the real wages
  • 00:24:48
    of many workers actually about half of
  • 00:24:51
    the US Labor Force is actually declining
  • 00:24:53
    as productivity is increasing so this is
  • 00:24:56
    a very jarring version of of non-shared
  • 00:25:01
    growth so therefore it confronted with
  • 00:25:04
    this picture a natural instinct is to
  • 00:25:07
    say why was there this shed prosperity
  • 00:25:10
    and why did it come apart so let me try
  • 00:25:12
    to answer that but before doing that let
  • 00:25:15
    me say that this is not the same across
  • 00:25:18
    countries but it's not unique to the
  • 00:25:19
    United States so in particular pretty
  • 00:25:21
    much every industrialized nation is
  • 00:25:23
    struggling with the same pattern
  • 00:25:25
    inequality has increased in most
  • 00:25:26
    countries us was the leader of
  • 00:25:28
    inequality and Remains the leader of
  • 00:25:30
    inequality but many other countries have
  • 00:25:32
    also
  • 00:25:34
    undergone similar processes and many
  • 00:25:37
    countries have struggled by the same
  • 00:25:40
    struggled with the same problem of
  • 00:25:43
    stagnant or sometimes even declining
  • 00:25:45
    like in Germany or in the UK real
  • 00:25:48
    incomes of people at the bottom of the
  • 00:25:50
    income
  • 00:25:51
    distribution so here let me go from the
  • 00:25:55
    British Industrial Revolution to the
  • 00:25:57
    early 20s Century
  • 00:25:59
    industrialization and go to the iconic
  • 00:26:01
    sector of the car manufacturing because
  • 00:26:04
    that was a leader in IND introducing
  • 00:26:07
    machinery and it was also a leader in
  • 00:26:10
    illustrating the types of shared
  • 00:26:14
    Prosperity dynamics that I emphasized
  • 00:26:16
    already in
  • 00:26:17
    particular you see here a picture from
  • 00:26:20
    the Ford motor Factory from 1919 Henry
  • 00:26:23
    Ford was at the very Forefront of
  • 00:26:25
    introducing mass production which meant
  • 00:26:27
    Machinery so he was doing the automation
  • 00:26:29
    he was introducing the he was you know
  • 00:26:32
    his factories were the first time there
  • 00:26:35
    were the gun Armament producers and
  • 00:26:39
    sewing machine producers that were doing
  • 00:26:40
    the same thing but he did at a much
  • 00:26:43
    bigger scale introducing the
  • 00:26:44
    interchangeable part system so some more
  • 00:26:47
    standardized production he introduced
  • 00:26:49
    decentralized electrical Machinery so
  • 00:26:50
    that machines could work much better and
  • 00:26:53
    much more
  • 00:26:53
    efficiently so he was very much in the
  • 00:26:56
    business of automation but also also
  • 00:26:58
    what you see with Henry Ford's in Henry
  • 00:27:01
    Ford's Factory is that while doing that
  • 00:27:04
    he also introduced and his Engineers
  • 00:27:06
    also introduced a completely different
  • 00:27:07
    way of in doing work much more technical
  • 00:27:10
    work much more engineering knowledge
  • 00:27:12
    both at the back office and on the front
  • 00:27:15
    line so no surprise that when you see
  • 00:27:17
    pictures from H Henry Ford's motor
  • 00:27:19
    Factory you see workers doing new tasks
  • 00:27:22
    new activities where their marginal
  • 00:27:25
    productivity was really Central for the
  • 00:27:27
    production process
  • 00:27:29
    as a result of these
  • 00:27:31
    tasks as a tremendous amount of new
  • 00:27:34
    machinery and investment was introduced
  • 00:27:36
    in the Auto industry employment
  • 00:27:38
    increased by
  • 00:27:39
    10-fold so this was employment growth
  • 00:27:42
    together with lots of new Machinery
  • 00:27:44
    being introduced but the second one was
  • 00:27:47
    very much related to power the Auto
  • 00:27:49
    industry wasn't just a leader in
  • 00:27:51
    Automation and new tasks but it was also
  • 00:27:53
    a leader in World labor organization in
  • 00:27:56
    a series of
  • 00:27:58
    uh iconic events labor organization
  • 00:28:01
    became most powerful in the Auto
  • 00:28:03
    industry and from there it spread to the
  • 00:28:06
    rest of the uh for to the rest of the
  • 00:28:08
    manufacturing industry there were a
  • 00:28:10
    number of very important ones uh this
  • 00:28:13
    one is the uh there was one in uh uh uh
  • 00:28:17
    in in Ford motor factories this is a
  • 00:28:19
    slightly later one you this is called
  • 00:28:21
    the sitdown strike where workers sat
  • 00:28:22
    down rather than doing the work and the
  • 00:28:24
    GM General Motors had to agree with
  • 00:28:26
    better working conditions more technical
  • 00:28:28
    work and and higher
  • 00:28:31
    wages so therefore my argument is that
  • 00:28:34
    the reason why we had this type of
  • 00:28:37
    growth both in the first half of the
  • 00:28:39
    20th century and in the decades
  • 00:28:41
    following World War II is precisely
  • 00:28:43
    because these two pillars of the
  • 00:28:44
    productivity bandwagon not just automate
  • 00:28:48
    work but create work for workers and the
  • 00:28:51
    power relations at least giving some
  • 00:28:53
    voice so that workers could not be
  • 00:28:56
    forced to work at low wages were both
  • 00:28:58
    important so why did then the shared
  • 00:29:01
    Prosperity come apart well because those
  • 00:29:04
    two pillars came apart here is how it
  • 00:29:06
    auto Factory looks like except for the
  • 00:29:09
    color this Machinery is very similar to
  • 00:29:12
    what Ford was doing it's a automation
  • 00:29:16
    Machinery during Ford's days it was
  • 00:29:18
    putting things together in very
  • 00:29:20
    rudimentary way that started being
  • 00:29:22
    mechanized here it's painting welding as
  • 00:29:25
    well as operation and assembly but
  • 00:29:27
    what's remarkable in this Auto Factory
  • 00:29:29
    is that you don't see the workers doing
  • 00:29:30
    the technical tasks so many employers
  • 00:29:34
    introduced the automated
  • 00:29:37
    Machinery but they didn't go to the next
  • 00:29:41
    step of introducing the new tasks hence
  • 00:29:43
    we took one more step towards that
  • 00:29:45
    modern Factory with the man and the
  • 00:29:47
    dog so technology became more biased
  • 00:29:51
    towards labor more substituting for
  • 00:29:53
    labor now of course the type of increase
  • 00:29:57
    inequality is an epocal event has many
  • 00:30:00
    causes but automation is really at the
  • 00:30:02
    heart of it and that's what my work with
  • 00:30:04
    Pascal Restrepo has argued so here is a
  • 00:30:07
    summary of that using this chart where
  • 00:30:09
    what I'm doing is I'm looking at more
  • 00:30:12
    detailed demographic groups distinguish
  • 00:30:14
    not just by gender and education but
  • 00:30:16
    gender education ethnicity and age and
  • 00:30:18
    I'm zeroing on that period from 1980 to
  • 00:30:21
    to the just before the covid period and
  • 00:30:24
    on the vertical axis I'm showing the
  • 00:30:26
    cumulative change in the real wages and
  • 00:30:29
    you can see that many of these circles
  • 00:30:30
    actually very big circles here meaning
  • 00:30:32
    big demographic groups are below the
  • 00:30:34
    zero line those are the people who are
  • 00:30:36
    losing out in terms of real incomes as
  • 00:30:39
    the economy is growing in terms of
  • 00:30:40
    productivity and the new element here is
  • 00:30:43
    the horizontal axis that's my and
  • 00:30:46
    pasqual's estimate of how much
  • 00:30:50
    automation has impacted that demographic
  • 00:30:52
    group it's roughly speaking the fraction
  • 00:30:54
    of tasks that the relevant demographic
  • 00:30:56
    group was performing in 1980 that have
  • 00:30:58
    since been automated and what you see is
  • 00:31:00
    a very strong relationship you know if
  • 00:31:02
    you want to understand why these groups
  • 00:31:04
    are doing so badly I think our evidence
  • 00:31:06
    shows you cannot do that without seeing
  • 00:31:08
    that uh these groups are the ones whose
  • 00:31:11
    jobs have been whose tasks have been
  • 00:31:13
    automated but power is also at the
  • 00:31:16
    center of it and power I want to now
  • 00:31:21
    argue
  • 00:31:23
    is really about two things in the modern
  • 00:31:27
    world it is
  • 00:31:29
    about
  • 00:31:31
    organization so United Auto Workers
  • 00:31:33
    sitdown strike was a step towards
  • 00:31:35
    strengthening labor so that they took a
  • 00:31:37
    slice from the productivity increases
  • 00:31:40
    but this the iconic event of the 1980s
  • 00:31:43
    the labor relation starts with the Petco
  • 00:31:45
    strike per professional air traffic
  • 00:31:46
    controller strike of 1981 where Ronald
  • 00:31:49
    Reagan as the new president fired all
  • 00:31:50
    the professional air traffic controllers
  • 00:31:52
    and that was a signal to many other
  • 00:31:54
    employers who similarly took a very
  • 00:31:56
    harsh line against TR strike activity
  • 00:31:59
    declin labor power decline unionization
  • 00:32:01
    started an accelerated decline after
  • 00:32:03
    that point although it was already
  • 00:32:04
    declining because of the decline the the
  • 00:32:07
    diminishing importance of
  • 00:32:08
    manufacturing but Power when I talk
  • 00:32:11
    about it is not just about unions it's
  • 00:32:14
    not certainly today in the United States
  • 00:32:17
    it's not about coercion you know there's
  • 00:32:18
    no slavery nobody in survi relations not
  • 00:32:21
    not many people anyway but it is also
  • 00:32:24
    about
  • 00:32:25
    ideology what sort of priority powerful
  • 00:32:28
    actors
  • 00:32:30
    have and the two book hens that
  • 00:32:35
    determined how gains in manufacturing
  • 00:32:38
    how gains in Industry got divided was
  • 00:32:43
    this but also the changing priorities
  • 00:32:46
    and ideologies of
  • 00:32:49
    businesses you can call it ideology you
  • 00:32:52
    can call it something else in the book
  • 00:32:53
    we call it Vision because ideology
  • 00:32:55
    sometimes has some bad
  • 00:32:56
    connotations but it was the changing
  • 00:33:00
    emphasis among managers and among owners
  • 00:33:05
    that good corporations were the lean
  • 00:33:08
    corporations that cut labor costs didn't
  • 00:33:11
    share the benefits with workers because
  • 00:33:13
    that would be waste and would reduce the
  • 00:33:15
    what the shareholders got and the more
  • 00:33:16
    you gave to shareholders the more
  • 00:33:17
    Dynamic the corporations became the more
  • 00:33:20
    Investments it led to and so on and so
  • 00:33:22
    forth and of course the symbol of that
  • 00:33:24
    was this great Economist Milton Freedman
  • 00:33:26
    but Milton Friedman's most famous
  • 00:33:28
    article wasn't one of those in scholarly
  • 00:33:30
    journals but it's something he wrote In
  • 00:33:32
    The New York Times magazine where he
  • 00:33:34
    said the only social responsibility of
  • 00:33:35
    business is to look after his
  • 00:33:36
    shareholders cut costs cut Wages that's
  • 00:33:39
    what's good and uh and it was a rallying
  • 00:33:42
    cry for many managers who for a variety
  • 00:33:45
    of reasons partly unions partly other
  • 00:33:48
    considerations Norms felt obliged that
  • 00:33:52
    if they make more profits if they export
  • 00:33:53
    more they'll share some of that with the
  • 00:33:55
    workers well fredman was telling them
  • 00:33:57
    you have license you don't have to do
  • 00:33:58
    that who will stand up against them well
  • 00:34:02
    unions could have done but unions were
  • 00:34:03
    also in Decline so these two book meant
  • 00:34:06
    that the power relations shifted against
  • 00:34:10
    labor so the reason why I went into some
  • 00:34:13
    details on these topics is
  • 00:34:16
    because I think to understand who will
  • 00:34:19
    benefit
  • 00:34:20
    from new technologies it is really
  • 00:34:24
    important to consider these two things
  • 00:34:26
    that have been drivers of who gets the
  • 00:34:30
    benefits in history whether new
  • 00:34:33
    technologies just automates or
  • 00:34:35
    introduces new tasks and hence increases
  • 00:34:37
    the marginal product of labor and how we
  • 00:34:39
    def power relations but when we think
  • 00:34:42
    about Technologies when I'm talking
  • 00:34:43
    about what here technology has two
  • 00:34:47
    parts what businesses are doing and
  • 00:34:50
    that's what I put the emphasis on way by
  • 00:34:52
    saying uh Milton Freedman you know this
  • 00:34:54
    is what you know people like Jack Welch
  • 00:34:57
    who were running these big businesses
  • 00:34:59
    but businesses of course can do with
  • 00:35:03
    technology what the nature of technology
  • 00:35:06
    or what the characteristics of the
  • 00:35:08
    technology allow and that is
  • 00:35:10
    determined by what the tech sector is
  • 00:35:13
    doing and that's becoming even more
  • 00:35:15
    important today with AI and advances in
  • 00:35:17
    digital Technologies and you might think
  • 00:35:20
    if the world is just one of seamlessly
  • 00:35:22
    working
  • 00:35:24
    markets all that that's important is
  • 00:35:26
    what's the profitable
  • 00:35:28
    strategy or perhaps if you believe Sam
  • 00:35:30
    Altman and uh and Mark Zuckerberg they
  • 00:35:33
    are also altruistic
  • 00:35:35
    so but actually in the same way that
  • 00:35:39
    ideology Vision priorities matter if you
  • 00:35:41
    believe fredman you cut wages if you
  • 00:35:45
    believe in what people used to call
  • 00:35:47
    welfare capitalism you share the gains
  • 00:35:48
    with the
  • 00:35:49
    workers well perhaps what the visions of
  • 00:35:54
    what the tech leaders do matters as well
  • 00:35:57
    and in fact in fact the argument in the
  • 00:35:58
    book and the reason for why I am worried
  • 00:36:01
    about the future is that it's not just a
  • 00:36:03
    problem of corporate America it's even
  • 00:36:05
    worse problem of tech America and the
  • 00:36:09
    problem of tech America which decides of
  • 00:36:11
    course the trajectory of technology is
  • 00:36:13
    not just for the us but throughout the
  • 00:36:15
    world is that as
  • 00:36:18
    a call it ideology call it Vision
  • 00:36:21
    preoccupation the whole tech industry
  • 00:36:24
    has become completely focused on
  • 00:36:26
    automation
  • 00:36:27
    why well I don't think you can
  • 00:36:29
    understand that without going back to
  • 00:36:30
    the origin stories of computer science
  • 00:36:32
    and AI it was This brilliant
  • 00:36:35
    mathematician Alan Turing who founded in
  • 00:36:39
    many ways the field of computer science
  • 00:36:41
    but he also had a very different way
  • 00:36:44
    according to you know relative to people
  • 00:36:46
    to his contemporaries of thinking about
  • 00:36:48
    both the human mind and the machines he
  • 00:36:50
    said his work was on computation he said
  • 00:36:53
    the human mind is just a Computing
  • 00:36:54
    machine and computers are Computing
  • 00:36:57
    machines we're going to build better and
  • 00:36:58
    better computers and ultimately the
  • 00:36:59
    computers are going to be as good as the
  • 00:37:01
    computer Computing machine that's in
  • 00:37:02
    your brain so that
  • 00:37:05
    framed the field of computer science as
  • 00:37:08
    one in which you judge the quality of
  • 00:37:12
    the machines by how much human parity
  • 00:37:15
    they achieve meaning how close they
  • 00:37:17
    become to humans and of course if you do
  • 00:37:19
    that it creates a natural bias for
  • 00:37:22
    automation the more human like it is or
  • 00:37:25
    the more it can take over human tasks
  • 00:37:29
    the better and that
  • 00:37:33
    became also the founding vision of the
  • 00:37:36
    field of AI where the the field was
  • 00:37:39
    defined created in
  • 00:37:41
    1956 uh before then nobody talks there's
  • 00:37:43
    no term of artificial intelligence these
  • 00:37:45
    gentlemen here who are also very famous
  • 00:37:48
    Define that field and they decare their
  • 00:37:50
    aim to be we're going to reach to human
  • 00:37:52
    brain and human capabilities in six
  • 00:37:54
    months okay perhaps a year all right for
  • 00:37:57
    a little bit optimistic but this vision
  • 00:38:00
    is completely dominant today in the tech
  • 00:38:03
    World artificial general intelligence
  • 00:38:06
    being its most
  • 00:38:08
    recent Offspring but even artificial
  • 00:38:10
    before artificial general intelligence a
  • 00:38:12
    lot of software systems a lot of
  • 00:38:15
    production processes that were digitized
  • 00:38:18
    were all about can we get computers to
  • 00:38:21
    do more of the tasks of humans and that
  • 00:38:23
    creates this bias towards Automation and
  • 00:38:24
    if the tech sector has the bias from
  • 00:38:26
    automation what will corporate leaders
  • 00:38:28
    do so there is a nuts and bolts problem
  • 00:38:32
    if you want to build a
  • 00:38:35
    better tool you need both nuts and bolts
  • 00:38:37
    but if there aren't the nuts aren't
  • 00:38:39
    there nobody is producing the uh
  • 00:38:42
    technologies that are going to be useful
  • 00:38:44
    for
  • 00:38:45
    humans bolts are useless but if bolts
  • 00:38:48
    are not there nobody wants to generate
  • 00:38:49
    the knots either so if if the corporate
  • 00:38:51
    leaders are believed to be just in the
  • 00:38:53
    business of cutting costs now if I stop
  • 00:38:57
    the at this
  • 00:38:59
    slide it would be very Bleak I'm saying
  • 00:39:03
    oh well you know ideology plus business
  • 00:39:05
    models they are condemning us to more
  • 00:39:07
    and more Automation and more and more
  • 00:39:08
    lower wages well
  • 00:39:10
    actually but from the very
  • 00:39:14
    beginning there was a very different
  • 00:39:16
    view of what technologies are about it
  • 00:39:19
    was as early as Alan Touring that
  • 00:39:22
    Norbert weiner a uh another brilliant
  • 00:39:24
    mathematician now this time at MIT was
  • 00:39:26
    writing about humans and computers
  • 00:39:29
    working together and in the book Simon
  • 00:39:32
    Johnson and I call this machine
  • 00:39:34
    usefulness to contrasted with the
  • 00:39:36
    machine intelligence and if you look at
  • 00:39:38
    history history of computers many of the
  • 00:39:41
    things that we completely depend on
  • 00:39:42
    today the computer mouse out of Douglas
  • 00:39:44
    angle Bart uh hyperlink hypertext menu
  • 00:39:47
    driven computers the arpanet and the
  • 00:39:49
    internet uh internet jcr lick liers work
  • 00:39:53
    they all came out to a very different
  • 00:39:54
    Vision where machines were not supposed
  • 00:39:56
    to replace humans
  • 00:39:57
    there wasn't uh human parity Obsession
  • 00:40:01
    but it was machines to be useful to
  • 00:40:04
    humans so the reason why I'm saying this
  • 00:40:07
    is because AI actually increases the
  • 00:40:11
    capabilities both for
  • 00:40:13
    automation if you want to try to
  • 00:40:16
    replicate
  • 00:40:18
    humans large language models are your
  • 00:40:20
    tool but it also increases amplifies the
  • 00:40:24
    possibilities for providing better
  • 00:40:26
    information to humans so so that they
  • 00:40:27
    can comp perform more complex tasks they
  • 00:40:29
    can make better decisions so therefore
  • 00:40:32
    this is actually a contrast between what
  • 00:40:36
    the tech industry is going to be about
  • 00:40:38
    that will matter greatly and choice is
  • 00:40:42
    going to be all over it now the problem
  • 00:40:45
    is that it's actually even worse if you
  • 00:40:48
    get to hang up with uh uh with machine
  • 00:40:53
    intelligence because what it we will end
  • 00:40:55
    up doing is that because you're so
  • 00:40:58
    convinced that machines are better than
  • 00:41:00
    humans you're going to rush to automate
  • 00:41:02
    a lot of tasks and what the US evidence
  • 00:41:05
    shows is that in almost every wave of
  • 00:41:09
    Technologies automation disappointed in
  • 00:41:12
    terms of
  • 00:41:12
    productivity so every wave of
  • 00:41:17
    Technology main frames Innovation uh
  • 00:41:20
    inventory
  • 00:41:21
    systems uh personal computers uh
  • 00:41:24
    software systems for office work they
  • 00:41:27
    all were introduced with great funfare
  • 00:41:29
    productivity is going to double and in
  • 00:41:32
    all cases it was lackluster productivity
  • 00:41:35
    and the reason is quite obvious if
  • 00:41:37
    humans are not as bad as you think and
  • 00:41:38
    machine intelligence is not so great
  • 00:41:41
    you're going to be over automating or
  • 00:41:43
    you're going to be excessively
  • 00:41:44
    automating but the problem is actually
  • 00:41:46
    worse also because again I keep coming
  • 00:41:50
    back the factory system was automating
  • 00:41:52
    but it was also about control but what
  • 00:41:54
    is ai ai is a control machine is AI is
  • 00:41:59
    an information tool and every
  • 00:42:00
    information tool just like Jeremy
  • 00:42:03
    bentham's panopticon is also a tool for
  • 00:42:05
    control so these two may look a world
  • 00:42:09
    apart the social credit system for
  • 00:42:12
    trains in
  • 00:42:13
    China and Facebook so some people will
  • 00:42:17
    say you know social media
  • 00:42:19
    democratization versus centralized
  • 00:42:21
    control but actually there's more
  • 00:42:22
    parallel between these two than meets
  • 00:42:24
    the eye both of those are that one or
  • 00:42:27
    organization with its own ideology is
  • 00:42:29
    centralizing information and it's
  • 00:42:31
    deciding what you see here it's telling
  • 00:42:35
    you well you see only the posts that are
  • 00:42:38
    approved and we're going to incentivize
  • 00:42:40
    that by giving you a higher credit if
  • 00:42:42
    you give the right posts and here you
  • 00:42:43
    see what we decide you see for purposes
  • 00:42:46
    of monetizing that with digital ads or
  • 00:42:48
    whatever El values the people who are
  • 00:42:50
    doing the content moderation in Facebook
  • 00:42:52
    or Google have so power which said was
  • 00:42:57
    Central is also completely Inseparable
  • 00:43:00
    from how we use AI so we're going to
  • 00:43:03
    have these choices who is going to get
  • 00:43:05
    more power with the use of AI and
  • 00:43:08
    whether we're going to use AI to
  • 00:43:09
    increase the marginal productivity and
  • 00:43:11
    the capabilities of workers or we're
  • 00:43:12
    going to try to sideline the workers and
  • 00:43:15
    history shows
  • 00:43:17
    we've have a lot of choices and today we
  • 00:43:20
    have a lot of choices for every example
  • 00:43:23
    or okay fine every 10 example of
  • 00:43:25
    automation I can find one good example
  • 00:43:27
    where some company or some sector has
  • 00:43:30
    used the same Technologies for creating
  • 00:43:32
    new tasks for workers for every Facebook
  • 00:43:35
    Google Chinese Communist party there is
  • 00:43:37
    the system in Taiwan where they have
  • 00:43:39
    used AI in order to make technology more
  • 00:43:43
    pro-democratic so there are choices what
  • 00:43:47
    choices are we going to
  • 00:43:48
    make
  • 00:43:50
    well that depends on who's going to make
  • 00:43:52
    the choices I think if the choices are
  • 00:43:53
    between Sam Alman and E musk I don't
  • 00:43:56
    think we're going to get the right
  • 00:43:58
    outcomes so the history again this time
  • 00:44:02
    is no different history teaches us one
  • 00:44:04
    thing Democratic process is really
  • 00:44:07
    important so if the debate is just
  • 00:44:09
    between Elon Musk and Sam Alman they can
  • 00:44:11
    go in the ring if they want but that's
  • 00:44:13
    not going to bring democracy and if you
  • 00:44:15
    don't bring democracy I don't think
  • 00:44:17
    you're going to have the type of broad
  • 00:44:19
    set of voices that say well we should
  • 00:44:22
    structure this technology both in terms
  • 00:44:24
    of the production process and in terms
  • 00:44:26
    of the relations of power such that a
  • 00:44:28
    broadc cross-section of society
  • 00:44:30
    including the developing World by the
  • 00:44:31
    way who that always gets invol that's
  • 00:44:34
    ignored that these people will benefit
  • 00:44:36
    so we need a democratic process where
  • 00:44:39
    will that Democratic process come from
  • 00:44:41
    well again labor movements but you know
  • 00:44:43
    I'm not sure whether Amazon and
  • 00:44:45
    Starbucks unionization is going to be
  • 00:44:47
    the kind of model so I think we need a
  • 00:44:50
    different approach to labor movement and
  • 00:44:52
    labor voice actually there is some
  • 00:44:54
    interesting changes in the United States
  • 00:44:56
    taking place where where union leaders
  • 00:44:58
    over the last two years have become much
  • 00:44:59
    more concerned about can we make the
  • 00:45:02
    labor movement be an input into the
  • 00:45:04
    technology Direction so that's the
  • 00:45:07
    thesis that I am defending but it will
  • 00:45:10
    also come
  • 00:45:11
    from Civil Society activism so Ral nater
  • 00:45:15
    before he became the punching bag for
  • 00:45:17
    spoiling the 2000 election was actually
  • 00:45:20
    a very important leader for civil
  • 00:45:22
    society because he was the face of the
  • 00:45:25
    consumer protection movement that that
  • 00:45:27
    was so important in introducing all
  • 00:45:28
    these pharmaceutical transport and and
  • 00:45:31
    other regulations so that sort of Civil
  • 00:45:34
    Society movement is going to be very
  • 00:45:35
    important and the issue is actually not
  • 00:45:38
    new at all in the first few years where
  • 00:45:42
    personal computers were coming on board
  • 00:45:44
    there were people like this gentleman
  • 00:45:45
    Ted Nelson who thought who outlin a very
  • 00:45:49
    different vision of personal computers
  • 00:45:52
    Ted Nelson and people like him thought
  • 00:45:54
    that the problem with information was
  • 00:45:57
    that companies like IBM were controlling
  • 00:45:59
    it and the personal computers were the
  • 00:46:01
    tool that would enable the
  • 00:46:02
    democratization of information both in
  • 00:46:04
    people's private lives and in the
  • 00:46:06
    production process but of course who
  • 00:46:09
    controlled the personal computers it was
  • 00:46:11
    you know Microsoft and other companies
  • 00:46:14
    that created large large companies that
  • 00:46:16
    created tools for other large companies
  • 00:46:18
    so the Ted Nelson hope never
  • 00:46:22
    materialized but I think perhaps in the
  • 00:46:23
    age of AI we can do
  • 00:46:25
    better and and the last thing I'm going
  • 00:46:27
    to show you
  • 00:46:30
    is that this is
  • 00:46:32
    not completely unprecedented either so
  • 00:46:36
    it's not a complete pipe dream to say
  • 00:46:39
    Civil
  • 00:46:40
    Society perhaps government
  • 00:46:42
    regulation Democratic input could then
  • 00:46:46
    shape the direction of
  • 00:46:48
    technology in a more beneficial
  • 00:46:50
    direction again we could go back to the
  • 00:46:52
    British industrial re second phase of
  • 00:46:53
    the British Industrial Revolution after
  • 00:46:54
    1840s 1850s but here's a more recent
  • 00:46:57
    example
  • 00:46:58
    energy the energy
  • 00:47:01
    sector you know okay fine we're not
  • 00:47:04
    doing all that much in climate change
  • 00:47:05
    about climate change we're probably
  • 00:47:07
    going to exceed two and a half% two and
  • 00:47:09
    2.5 degrees Centigrade over
  • 00:47:10
    preindustrial times but today actually
  • 00:47:13
    the world is much better than it was 20
  • 00:47:15
    years ago because 20 years ago none of
  • 00:47:17
    the renewable Technologies were even
  • 00:47:19
    close to being cost competitive in
  • 00:47:22
    electricity production they were about
  • 00:47:23
    10 times as expensive and starting in
  • 00:47:27
    the late
  • 00:47:29
    2000s there's a complete Decline and now
  • 00:47:32
    for electricity
  • 00:47:35
    production onshore wind offshore wind
  • 00:47:37
    and different types of Solar
  • 00:47:38
    Technologies are all cost competitive
  • 00:47:40
    with fossil fuels how did that happen it
  • 00:47:42
    happened first of all because of
  • 00:47:44
    innovation there was a huge number of
  • 00:47:46
    patents and great exploitation of
  • 00:47:49
    economies of scale in solar panel
  • 00:47:51
    production and some other tool
  • 00:47:52
    production why did it happen well
  • 00:47:55
    because there was pressure from Civil
  • 00:47:57
    Society a few countries introduced uh
  • 00:48:00
    carbon taxes some countries including
  • 00:48:03
    starting in California introduced
  • 00:48:04
    regulations that made fossil fuels less
  • 00:48:06
    profitable and many countries including
  • 00:48:09
    the US started providing subsidies to
  • 00:48:11
    the more socially beneficial direction
  • 00:48:13
    of research which means Renewables
  • 00:48:15
    rather than fossil fuels so what
  • 00:48:18
    happened in energy I think can happen in
  • 00:48:20
    how we use Ai and I I think the past
  • 00:48:24
    shows that automatic hope that new
  • 00:48:27
    technologies will naturally benefit
  • 00:48:29
    democracy will naturally benefit workers
  • 00:48:31
    will naturally benefit all of us is a
  • 00:48:33
    little bit too much wishful thinking but
  • 00:48:35
    the potential is there and if we create
  • 00:48:38
    the right Power Balance and the right
  • 00:48:40
    direction for technology we have more
  • 00:48:42
    hope thank
  • 00:48:53
    you thank you Darren so now we're moving
  • 00:48:55
    into the the Q and
  • 00:48:57
    portion and we've got about 20 25
  • 00:48:59
    minutes um to uh to hear from all of you
  • 00:49:04
    and uh and and hear more from from
  • 00:49:06
    Darren so uh as I mentioned at the start
  • 00:49:09
    if you want to post some questions uh uh
  • 00:49:13
    essentially anonymously you're you're
  • 00:49:14
    very welcome to do so uh using that QR
  • 00:49:18
    code from uh from your phone um but
  • 00:49:21
    we've also got microphones around the
  • 00:49:22
    audience so so maybe um we start with an
  • 00:49:25
    audience question if I can can see any
  • 00:49:27
    any
  • 00:49:28
    hands okay so I don't know where the
  • 00:49:30
    microphones are though so oh here we go
  • 00:49:33
    so let's let's start over here because
  • 00:49:34
    it's probably easiest for you to get
  • 00:49:36
    to and uh and we'll go from there uh hi
  • 00:49:40
    thanks very much that was really
  • 00:49:41
    insightful uh I was wondering about
  • 00:49:43
    power not only in an ideological sense
  • 00:49:46
    but also in the sense of Market power
  • 00:49:48
    and whether the emergence of new
  • 00:49:50
    technologies and the r in productivity
  • 00:49:52
    may be associated with the emergence of
  • 00:49:55
    Superstar firms and consequently
  • 00:49:57
    concentrated labor markets may actually
  • 00:49:59
    lead to a depression in wages I wonder
  • 00:50:01
    if you think that's a realistic concern
  • 00:50:03
    and Absolut should be consider
  • 00:50:04
    absolutely uh thank you very much for
  • 00:50:05
    that question and in fact there is a
  • 00:50:08
    third reason why technological change
  • 00:50:10
    may not
  • 00:50:13
    generate prosperity for regular people
  • 00:50:16
    or workers and it is related to Market
  • 00:50:18
    power so if you have a new techn so you
  • 00:50:20
    know when we're talking of prosperity
  • 00:50:22
    here we're talking of real wages wages
  • 00:50:24
    divided by some sort of priceing de so
  • 00:50:27
    imagine we have a new technology but at
  • 00:50:29
    the same time it increases the market
  • 00:50:30
    power of a few companies so they start
  • 00:50:32
    charging higher markups higher prices so
  • 00:50:34
    then the price declines that would have
  • 00:50:36
    led to the real wage increase would not
  • 00:50:39
    happen and I think that is a concern and
  • 00:50:41
    indeed as you said there are many
  • 00:50:43
    sectors where we are seeing greater
  • 00:50:46
    concentration the reason why I have not
  • 00:50:47
    emphasized it is because emphasizing two
  • 00:50:50
    rather than three is a little simpler
  • 00:50:51
    but also because many of the tech
  • 00:50:54
    sectors are not monetizing
  • 00:50:57
    their products via higher prices and
  • 00:51:00
    that's part of the Paradox of antitrust
  • 00:51:02
    today you know uh there are people like
  • 00:51:05
    Lina Khan and Jonathan canther in the
  • 00:51:07
    United States who are really worrying
  • 00:51:09
    about these things but there's a big
  • 00:51:10
    barrier against them which is the usual
  • 00:51:13
    antitrust argument would be well you
  • 00:51:15
    know you've come to Corner the market
  • 00:51:17
    you're charging higher prices well
  • 00:51:18
    Facebook doesn't change any prices it
  • 00:51:20
    just takes your data and monetizes it
  • 00:51:22
    Google doesn't charge any prices Amazon
  • 00:51:24
    actually well you know it's charges
  • 00:51:27
    prices but often times it cuts prices
  • 00:51:29
    relative to competitors because they're
  • 00:51:30
    monetizing it differently they're also
  • 00:51:33
    the Venture Capital based model of grow
  • 00:51:36
    very fast is creating a long range of
  • 00:51:39
    long time period in which these
  • 00:51:40
    companies are not really charging big
  • 00:51:42
    markups so I think it's important but
  • 00:51:44
    that's why I haven't emphasized that as
  • 00:51:46
    much but on the other hand I would say
  • 00:51:49
    that business model of monetizing data
  • 00:51:51
    actually really is pricious because it
  • 00:51:54
    closes the door or it makes it hard
  • 00:51:56
    harder for the types of tools that I was
  • 00:51:59
    talking about that would make workers
  • 00:52:01
    more productive why because if the main
  • 00:52:03
    way of making money is collect data from
  • 00:52:05
    people and monetize that through digital
  • 00:52:07
    ads then you don't put your energy into
  • 00:52:09
    finding better ways of making workers
  • 00:52:11
    more
  • 00:52:12
    productive so let me let me come to a
  • 00:52:14
    question that's related that I've got
  • 00:52:17
    here and um although just to to tea up
  • 00:52:19
    the next question hands up okay maybe
  • 00:52:22
    we'll take some maybe down here next but
  • 00:52:25
    but this is I think somewhat related um
  • 00:52:28
    someone on on on here is asking or
  • 00:52:30
    suggesting that maybe um that early on
  • 00:52:34
    benefits of Technology are more
  • 00:52:36
    concentrated um but maybe later on and
  • 00:52:39
    they're suggesting maybe it's a few
  • 00:52:41
    decades but I don't know um you know
  • 00:52:44
    there's a delay and then that spreads
  • 00:52:46
    what what what do you think well that's
  • 00:52:47
    a great question it's a very very
  • 00:52:48
    important point so the question is
  • 00:52:51
    obviously the British Industrial
  • 00:52:53
    Revolution narrative that I provided
  • 00:52:54
    suggests that you know early on things
  • 00:52:56
    didn't work out and later they worked
  • 00:52:58
    out but I think the question is was that
  • 00:52:59
    automatic yeah or was that the result of
  • 00:53:02
    some difficult choices difficult
  • 00:53:04
    institutional adjustments so we can't
  • 00:53:06
    know that we don't have the
  • 00:53:07
    counterfactual history but
  • 00:53:09
    my guess is that a if we did not create
  • 00:53:13
    the trade unions if the trade unions
  • 00:53:15
    they were persecuted as heavily in the
  • 00:53:17
    beginning of the 20th century in the UK
  • 00:53:19
    as they were in the beginning of the
  • 00:53:20
    19th century if UK did not democratize
  • 00:53:24
    and if the emphasis remained on
  • 00:53:26
    automation it would have happened even
  • 00:53:28
    more slowly and you know 90 years is
  • 00:53:31
    already pretty slow that's three
  • 00:53:32
    generations so my argument is that we
  • 00:53:35
    can do it much faster with the right
  • 00:53:37
    technological choices and right
  • 00:53:38
    institutional choices so I wouldn't say
  • 00:53:40
    it's automatic but sure there are many
  • 00:53:43
    adjustments that do take place Norms
  • 00:53:44
    change institutions change uh technology
  • 00:53:46
    changes and there are Market processes
  • 00:53:48
    that work more slowly absolutely I think
  • 00:53:50
    I'm not ruling those out but I think the
  • 00:53:52
    automatic is not the just the the only
  • 00:53:55
    order of the day here here okay question
  • 00:53:58
    over here
  • 00:53:59
    please sorry thank you uh uh Professor
  • 00:54:04
    uhu um it's it's more not question but
  • 00:54:07
    more like I wonder how uh this kind of
  • 00:54:11
    uh British revolution uh Industrial
  • 00:54:13
    Revolution situation is leading us to
  • 00:54:15
    like create a fear mongering of somebody
  • 00:54:18
    or something stealing away our job like
  • 00:54:21
    in the past people were so afraid that
  • 00:54:23
    their job is going to be taken away by
  • 00:54:24
    machine and now it's people are afraid
  • 00:54:27
    again that it's going to be taken away
  • 00:54:28
    by the AI my my comment is uh Professor
  • 00:54:32
    is that uh in the past people have to
  • 00:54:36
    use uh their muscle to work for the
  • 00:54:38
    betterment of themselves and then it it
  • 00:54:41
    evolves into the usage of brain and
  • 00:54:43
    maybe in the future it's going to be
  • 00:54:45
    involved into something else that is
  • 00:54:46
    like perhaps we no longer need to be
  • 00:54:49
    busy about doing something clal for
  • 00:54:50
    example that is do uh is going to be
  • 00:54:52
    taken away by the AI but then human
  • 00:54:55
    Humanity in the future could be more
  • 00:54:57
    focused on something that is more useful
  • 00:54:59
    or more um using the heart instead of
  • 00:55:02
    the brain if if if you get my uh Point
  • 00:55:05
    uh my sense yeah I I get your point and
  • 00:55:08
    and but but but I partially disagree uh
  • 00:55:11
    first of all I mean I think you know
  • 00:55:14
    there's a thin line separating concerns
  • 00:55:17
    and fear mongering so I would say
  • 00:55:19
    concerns are different than fear
  • 00:55:21
    mongering if we are also seeing a path
  • 00:55:26
    that would avoid those concerns so there
  • 00:55:29
    have been indeed you are 100% right
  • 00:55:31
    periodic concerns about jobs
  • 00:55:34
    disappearing but they haven't all been
  • 00:55:36
    wrong some of them have been right some
  • 00:55:37
    of them have been wrong so the evidence
  • 00:55:40
    that I did not show but it's my uh my
  • 00:55:42
    work with Pascal Restrepo for
  • 00:55:45
    example uh when we looked at the
  • 00:55:48
    introduction of robots which was the
  • 00:55:50
    previous big technology that uh that uh
  • 00:55:54
    people were very afraid of well they
  • 00:55:56
    were right to be afraid of in the United
  • 00:55:58
    States we find that for every one robot
  • 00:56:00
    there were six jobs that were destroyed
  • 00:56:02
    and uh and and net and uh and in local
  • 00:56:06
    labor markets where robots were
  • 00:56:08
    introduced there were significantly
  • 00:56:09
    lower wages especially for workers who
  • 00:56:10
    were engaged in manual
  • 00:56:12
    tasks and uh but it's not in inevitable
  • 00:56:16
    so in Germany when they introduced
  • 00:56:18
    robots what they did at the same time
  • 00:56:20
    especially in work in companies which
  • 00:56:22
    had unions and work councils is that
  • 00:56:25
    they also at the same time upgraded the
  • 00:56:27
    work jobs so the workers who used to do
  • 00:56:29
    say painting or welding now became
  • 00:56:31
    technical workers and as a result the
  • 00:56:33
    effects were not as negative so they
  • 00:56:35
    were right workers were right to be
  • 00:56:37
    concerned but there was another option
  • 00:56:39
    now I think in my mind the fear
  • 00:56:41
    mongering is when we're talking of
  • 00:56:43
    artificial general intelligence and
  • 00:56:44
    Killer Robots that's fear mongering
  • 00:56:46
    because it doesn't give us much choice
  • 00:56:47
    and it's a very by you know uh uh uh d
  • 00:56:52
    false dichotomy you know either robots
  • 00:56:55
    are either super super artificial
  • 00:56:58
    intelligence is going to be great for us
  • 00:56:59
    or it's going to kill us all so I think
  • 00:57:01
    the issue is in the middle it's going to
  • 00:57:03
    do nether but it's how we actually deal
  • 00:57:06
    with it and you are absolutely right
  • 00:57:08
    there is there was a hope early on that
  • 00:57:12
    machines would take manual Works manual
  • 00:57:15
    jobs heavy jobs and they will leave us
  • 00:57:17
    with
  • 00:57:18
    more uh enjoyable more satisfying and
  • 00:57:22
    less dangerous jobs and some of that has
  • 00:57:24
    happened in workplaces where robots have
  • 00:57:27
    been introduced in the US and in Europe
  • 00:57:29
    yes in the US I told you jobs have
  • 00:57:31
    disappeared but remaining jobs became
  • 00:57:33
    safer and we definitely don't want to go
  • 00:57:35
    to the time where people carry things by
  • 00:57:37
    hands when there are cranes and uh uh
  • 00:57:39
    and and and and and automated machinery
  • 00:57:42
    for carrying but the problem with AI is
  • 00:57:44
    actually that it's not taking the the
  • 00:57:47
    jobs that are more mundane you know if
  • 00:57:49
    you look at what are the jobs that are
  • 00:57:51
    affected by AI you know the safest jobs
  • 00:57:53
    are custodial Protective Services
  • 00:57:55
    constru ruction workers it's the office
  • 00:57:58
    jobs it's some jobs that are semi
  • 00:58:00
    creative that could be taken away by AI
  • 00:58:02
    if we just go down the automation path
  • 00:58:05
    so so it's actually and then yeah of
  • 00:58:07
    course perhaps in 100 years time we can
  • 00:58:10
    have other adjustments but but again
  • 00:58:12
    it's the automatic versus non automatic
  • 00:58:14
    issue so I come to another poster
  • 00:58:17
    question um really again I think an
  • 00:58:19
    inequality around access to AI um and so
  • 00:58:23
    you know we might all be sitting here
  • 00:58:25
    and it's great we can access CH GPT or
  • 00:58:28
    co-pilot or Claude or gemini or whatever
  • 00:58:30
    it is for whatever purpose great but
  • 00:58:33
    that's not true everywhere around the
  • 00:58:35
    world so the question is uh how will the
  • 00:58:37
    introduction of AI influence the
  • 00:58:38
    economic landscape of regions with
  • 00:58:40
    limited technological access
  • 00:58:42
    particularly in contrast to the
  • 00:58:43
    advancements seen in more affluent
  • 00:58:45
    Nations absolutely that's very important
  • 00:58:46
    and that's what that's the thing I very
  • 00:58:48
    briefly hinted at which is what about
  • 00:58:50
    the developing World developing world
  • 00:58:52
    doesn't have access even if they have
  • 00:58:53
    access the global division of labor is
  • 00:58:55
    going to change in massive way you know
  • 00:58:57
    what are the you know look at the
  • 00:59:00
    globalization experience of the uh 18th
  • 00:59:04
    century India became hugely
  • 00:59:07
    de-industrialized that was like the uh
  • 00:59:10
    place that was most advanced in textiles
  • 00:59:13
    globalization became uh very damaging to
  • 00:59:16
    India well look at the globalization in
  • 00:59:19
    the 1960s 1970s well South Korea Taiwan
  • 00:59:23
    later China did very well under
  • 00:59:25
    globalization why globalization created
  • 00:59:28
    opportunities that they didn't have in
  • 00:59:30
    particular it enabled them to specialize
  • 00:59:32
    in things like textiles or uh or
  • 00:59:34
    furniture or toys that were low skill
  • 00:59:38
    low technology sectors that could be an
  • 00:59:41
    engine of growth well the question is is
  • 00:59:43
    AI going to create that sort of thing
  • 00:59:45
    and it doesn't seem like that quite the
  • 00:59:47
    opposite some of the services that could
  • 00:59:49
    be done in the in the developing world
  • 00:59:51
    would actually be completely reshored
  • 00:59:53
    with AI so I think the complic
  • 00:59:55
    implications are are going to be uh more
  • 00:59:58
    complex but even having access to
  • 01:00:01
    Ai and knowing how to use it may not be
  • 01:00:04
    enough so imagine that we have you know
  • 01:00:07
    uh uh let me give you an example from
  • 01:00:12
    journalism so we have 100,000
  • 01:00:14
    journalists say in the United States
  • 01:00:16
    that's not not quite that many but okay
  • 01:00:19
    but we we teach all of them to be very
  • 01:00:21
    good at prompt
  • 01:00:22
    engineering but if ai go in the
  • 01:00:26
    automation Direction and all that it can
  • 01:00:28
    do for journalists is that more of their
  • 01:00:30
    tasks can be completed doesn't matter
  • 01:00:32
    whether you're very good at prompt
  • 01:00:34
    engineering what's going to happen is
  • 01:00:35
    that we're not going to need 100,000 but
  • 01:00:36
    we're going to need not only 20,000
  • 01:00:38
    journalists so journalists are not going
  • 01:00:40
    to fare very well what would it take for
  • 01:00:43
    them to Fair better well if instead of
  • 01:00:44
    just automating if we create AI if we
  • 01:00:47
    develop AI such that we can actually
  • 01:00:49
    provide better information to journalist
  • 01:00:50
    so that they can do more sophisticated
  • 01:00:52
    research so that's really the nature of
  • 01:00:55
    Technology whether you get you know how
  • 01:00:56
    to use the technology or not is
  • 01:00:58
    important but it's not just the only
  • 01:01:00
    determining Factor okay another question
  • 01:01:02
    from the room how about we go um I
  • 01:01:05
    already have a microphone okay whoever's
  • 01:01:07
    got a microphone I can't see where you
  • 01:01:08
    are hi oh there you are fantastic um hi
  • 01:01:11
    my name is Julia I'm an undergraduate
  • 01:01:12
    here studying economics um my question
  • 01:01:15
    relates to a different type of
  • 01:01:16
    inequality you show a graph with the
  • 01:01:19
    Divergence in Ro wages like related to
  • 01:01:22
    education um and noticeably there's less
  • 01:01:25
    Divergence among women women linking
  • 01:01:27
    that in with what you say about task
  • 01:01:29
    replacement um is the implication that
  • 01:01:32
    tasks typically perform by women are
  • 01:01:34
    being replaced to a lesser extent or how
  • 01:01:36
    would you explain that di absolutely so
  • 01:01:38
    essentially 100% you're very very
  • 01:01:40
    perceptive that if you look at the
  • 01:01:43
    period that I focused on which is where
  • 01:01:45
    some ofice work was being automated with
  • 01:01:47
    Software System but a lot of automation
  • 01:01:49
    was taking place for blue color jobs
  • 01:01:52
    that was on more the onus was heavier on
  • 01:01:55
    men and in fact you're 100% right pretty
  • 01:01:59
    much every dimension of
  • 01:02:01
    inequality increase except by gender the
  • 01:02:04
    gender
  • 01:02:04
    inequality narrowed for other reasons
  • 01:02:07
    but the automation of male jobs
  • 01:02:09
    especially male jobs that paid High
  • 01:02:11
    wages help but here is the bad news I
  • 01:02:14
    have recently done the study of AI and
  • 01:02:17
    who might be you know it's very you know
  • 01:02:21
    speculative because we don't it hasn't
  • 01:02:22
    happened yet it's the future but if you
  • 01:02:24
    look at the types of jobs that generate
  • 01:02:26
    AI can perform they're more female jobs
  • 01:02:28
    so it looks like now generative AI might
  • 01:02:32
    act towards increasing the gender gap
  • 01:02:34
    rather than the earlier automation that
  • 01:02:36
    might have a little bit closed
  • 01:02:38
    it okay I got another one from the room
  • 01:02:42
    I know maybe go to
  • 01:02:46
    you thank you and please excuse my voice
  • 01:02:49
    it's about to be gone but um I wanted to
  • 01:02:52
    ask you about that future where you have
  • 01:02:54
    that one person who's who watching the
  • 01:02:56
    dog you know and you don't know you
  • 01:02:58
    don't have all these other workers how
  • 01:03:00
    does the the notion of a universal basic
  • 01:03:02
    wage fit into that and what would be
  • 01:03:04
    differences between countries yeah so
  • 01:03:07
    excellent question and thank you for
  • 01:03:08
    bringing it up you know because there's
  • 01:03:12
    like a big gaping hole in what I talked
  • 01:03:14
    about which is okay fine why do we need
  • 01:03:17
    wages to create shared Prosperity let
  • 01:03:20
    Elon Musk earn everything okay some
  • 01:03:22
    outman earns some too and then we
  • 01:03:24
    redistribute all of it
  • 01:03:26
    and then you know you can choose your
  • 01:03:28
    favorite method of redistribution could
  • 01:03:29
    be Ubi it could be something else but we
  • 01:03:32
    have a huge level of
  • 01:03:34
    inequality and then we just use the
  • 01:03:36
    fiscal system to redistribute it what's
  • 01:03:39
    wrong with that well I would say there
  • 01:03:41
    are three things wrong with that first
  • 01:03:43
    of all I'm suggesting an alternative
  • 01:03:45
    which is redirecting technology so that
  • 01:03:47
    we generate more equal distribution of
  • 01:03:48
    income so saying that we're going to go
  • 01:03:51
    to Ubi is defe in my opinion because it
  • 01:03:54
    says we can't do anything else there is
  • 01:03:56
    nothing we can do to change technology
  • 01:03:58
    or technology is
  • 01:04:00
    unchangeable the future is one of only
  • 01:04:02
    Elon Musk some Alman and Mark Zuckerberg
  • 01:04:05
    making money were going to be all
  • 01:04:07
    dispensable well that's a sad type of
  • 01:04:10
    defeatism second I don't think po
  • 01:04:13
    political economy of it doesn't work I
  • 01:04:15
    haven't seen many billionaires who are
  • 01:04:17
    willingly giving their money today I
  • 01:04:18
    mean the only thing that they are
  • 01:04:19
    willing to do is Charity which often is
  • 01:04:24
    a way of f further boosting their
  • 01:04:27
    Prestige and
  • 01:04:28
    power so what makes you think that
  • 01:04:33
    tomorrow suddenly they'll say we earn
  • 01:04:34
    everything but we're going to distribute
  • 01:04:36
    the rest of it well perhaps if they're
  • 01:04:37
    really worried about a revolution
  • 01:04:39
    perhaps but it's not going to be that
  • 01:04:41
    easy but the third one is the one that
  • 01:04:43
    would really worry me and that again
  • 01:04:45
    takes us from the realm of pure
  • 01:04:48
    economics to more broader social
  • 01:04:50
    considerations if we create a world or
  • 01:04:52
    if we are in the world in which say 5%
  • 01:04:57
    of the population does the earnings the
  • 01:05:00
    remaining 95% is completely useless they
  • 01:05:02
    don't have any jobs they can't do
  • 01:05:05
    anything they just live off the crumbs
  • 01:05:07
    that's going to be a hugely hierarchical
  • 01:05:09
    hugely dystopian world with status gaps
  • 01:05:13
    that are just massive even relative to
  • 01:05:15
    what we are seeing today so I don't
  • 01:05:16
    think that's going to be a happy world
  • 01:05:18
    so yes if indeed either because I'm
  • 01:05:21
    wrong and it's inevitable that we go to
  • 01:05:23
    massive inequality or it's not
  • 01:05:25
    inevitable but we don't make the right
  • 01:05:27
    policy choices yes of course we have to
  • 01:05:29
    create some sort of way of
  • 01:05:30
    redistributing them but hopefully it
  • 01:05:32
    doesn't come to that thank you for
  • 01:05:33
    raising that let's do another one from
  • 01:05:36
    the room okay can we go down the front
  • 01:05:39
    here
  • 01:05:44
    please I think both of you had your hand
  • 01:05:46
    up so you can find it out it's up to you
  • 01:05:49
    thank you um thank you for your talk um
  • 01:05:53
    I want to ask that um as we have Cod in
  • 01:05:55
    your talk Milton Freedman um clearly
  • 01:05:59
    thinks that market mechanism is a better
  • 01:06:01
    arbitrator of um Power Balance and stuff
  • 01:06:05
    than political channels but also I feel
  • 01:06:08
    that there are also problems with um
  • 01:06:11
    politics because uh sorry market
  • 01:06:13
    mechanism because uh clearly economic
  • 01:06:16
    power is very approximate to political
  • 01:06:18
    power and um as we see um more and more
  • 01:06:22
    technology techn attack Giants Rising
  • 01:06:25
    maybe the legislations that um are
  • 01:06:28
    supposed to curb their power are like
  • 01:06:30
    received the most influence from them so
  • 01:06:33
    um who do you think is the better
  • 01:06:36
    arbitrator of this power B well I don't
  • 01:06:38
    think there is a perfect arbitrator you
  • 01:06:40
    you know absolutely I I think there's
  • 01:06:42
    sometimes in discussions and sometimes
  • 01:06:45
    in economics debates there is this sort
  • 01:06:49
    of rhetoric that somehow politics is
  • 01:06:53
    something that comes in addition
  • 01:06:56
    like on top like things are working well
  • 01:06:59
    if only politics wasn't part of it and I
  • 01:07:01
    think that's not the right way to think
  • 01:07:03
    about it politics is with us all the
  • 01:07:04
    time politics power relations are always
  • 01:07:07
    there in workplaces in markets in social
  • 01:07:10
    situations and it is a balance between
  • 01:07:12
    the market and politics and exactly like
  • 01:07:14
    you said if somebody has a lot of
  • 01:07:15
    economic power they will tend to have a
  • 01:07:17
    lot of political power so saying oh the
  • 01:07:20
    Market's going to sort out things if we
  • 01:07:22
    just leave politics out is is a fantasy
  • 01:07:26
    but the political process is very
  • 01:07:27
    unequal sometimes much more unequal than
  • 01:07:29
    the economic process so that's why we
  • 01:07:31
    have to try to build the right sort of
  • 01:07:33
    Institutions and Norms to balance them
  • 01:07:35
    out but it's not going to be perfect and
  • 01:07:37
    when unions were
  • 01:07:39
    strong they
  • 01:07:41
    helped curb some abuses of workers but
  • 01:07:44
    then the workers
  • 01:07:46
    sometimes had too much power in some
  • 01:07:48
    workplaces and they use that power
  • 01:07:51
    incorrectly or badly like resisting
  • 01:07:53
    technological change in the British
  • 01:07:55
    printing in the British printing
  • 01:07:57
    industry uh unions resisted printing
  • 01:08:00
    machines making the industry fall behind
  • 01:08:03
    20 years so any group having too much
  • 01:08:06
    power is going to be uh a recipe for
  • 01:08:10
    certain types
  • 01:08:12
    of problematic outcomes so some sort of
  • 01:08:15
    balance if we can achieve it would be
  • 01:08:17
    the thing to strive for okay so let me
  • 01:08:22
    let me go with another question that
  • 01:08:23
    somebody's put uh online here um this is
  • 01:08:27
    this getting to what you're talking
  • 01:08:28
    about before around essentially you know
  • 01:08:30
    one one view of AI is substituting
  • 01:08:32
    humans versus the other one
  • 01:08:34
    complimenting or or augmenting um and
  • 01:08:36
    and the question is really just about
  • 01:08:38
    well what what skills do you think will
  • 01:08:39
    see we will see complimented versus
  • 01:08:41
    substituted by Ai and I guess really
  • 01:08:44
    they're getting out is how do we then
  • 01:08:46
    think about that in the context of
  • 01:08:47
    directing or redirecting technological
  • 01:08:50
    development into sort of the the right
  • 01:08:53
    way shall we say as opposed to the other
  • 01:08:54
    perfect there a great great question but
  • 01:08:56
    my answer is it will depend because it
  • 01:08:58
    really depends on how we develop AI so
  • 01:09:00
    here is one skill that I would love to
  • 01:09:03
    see as complement to AI
  • 01:09:07
    electricians
  • 01:09:09
    so in the United Kingdom in the US we
  • 01:09:12
    have a shortage of electricians that's
  • 01:09:13
    going to get only worse with the
  • 01:09:14
    electrification of the
  • 01:09:16
    grid what is the problem the problem is
  • 01:09:18
    we're not training enough electricians
  • 01:09:20
    and the electricians are required to
  • 01:09:22
    deal with more and more complex problems
  • 01:09:27
    so is that a substitute or compliments
  • 01:09:29
    to AI well you can say well we can try
  • 01:09:31
    to automate these jobs in which case
  • 01:09:32
    they would be substitutes but actually
  • 01:09:34
    the better way would be use AI to
  • 01:09:36
    provide better information and better
  • 01:09:38
    training opportunities so that they can
  • 01:09:39
    upgrade their skills they can recognize
  • 01:09:41
    problems and diagnose problems and
  • 01:09:43
    perform more sophisticated tasks in
  • 01:09:45
    which case AI would be a compliment to
  • 01:09:47
    electricians same thing for plumbers
  • 01:09:49
    same things to other manual workers so I
  • 01:09:51
    think there's a lot of scope for
  • 01:09:52
    creating those complementarities but it
  • 01:09:54
    would require the technology to change
  • 01:09:56
    in that
  • 01:09:58
    direction okay I think we got time for
  • 01:10:00
    maybe one more question we'll take that
  • 01:10:02
    from the room um so let's go in the very
  • 01:10:06
    back because I've had a front bias here
  • 01:10:08
    so let's go up the back there um and
  • 01:10:11
    apologies to the microphone people
  • 01:10:13
    having to walk up the
  • 01:10:17
    stairs very interesting thank you for
  • 01:10:19
    your time I'm studying artificial
  • 01:10:21
    intelligence for business here um the
  • 01:10:24
    question is
  • 01:10:26
    you're talking you're talking about the
  • 01:10:29
    the the capabilities that are
  • 01:10:32
    complimenting humans through Ai and the
  • 01:10:36
    and the ones that replace uh and you
  • 01:10:39
    talk about Powers corporate Powers
  • 01:10:42
    industrial Powers I think there is a
  • 01:10:44
    double narrative at the same time
  • 01:10:47
    happening because when you see
  • 01:10:50
    yesterday H the LinkedIn and Microsoft
  • 01:10:53
    work report telling that 7 5% of white
  • 01:10:56
    colors are using AI in in the United
  • 01:11:00
    States but at the same time h two thirds
  • 01:11:03
    of the companies in the United States
  • 01:11:05
    are developing projects to replace some
  • 01:11:10
    of their Workforce through
  • 01:11:12
    AI I my question is in this double
  • 01:11:18
    narrative ER from your point of view
  • 01:11:20
    which one is more uh
  • 01:11:23
    realistic the AI complementing
  • 01:11:26
    capabilities of humans or the companies
  • 01:11:29
    making Replacements well that's a great
  • 01:11:31
    question and that's the key question and
  • 01:11:32
    I don't think I can make a forecast
  • 01:11:36
    because it's not like the weather it's
  • 01:11:38
    something we're going to choose
  • 01:11:40
    so if Microsoft and open Ai and Google
  • 01:11:45
    decide it's substituting for workers
  • 01:11:48
    then we're going to end up with
  • 01:11:50
    substituting for workers unless some
  • 01:11:52
    other companies come up so their choices
  • 01:11:54
    are going to matter
  • 01:11:55
    but also I think hype about AI is not
  • 01:11:59
    helping and and the number that you just
  • 01:12:02
    quoted is pure hype you know
  • 01:12:06
    uh very few workers right now are really
  • 01:12:09
    using AI in the United States so you can
  • 01:12:11
    count many workers using AI because if
  • 01:12:13
    you're using Microsoft Word Microsoft
  • 01:12:14
    Word has a co-pilot the co-pilot has AI
  • 01:12:17
    in it so then you can say you do but I
  • 01:12:20
    uh arranged a big with my collaborators
  • 01:12:24
    big survey for all of businesses in the
  • 01:12:27
    United States through the Census Bureau
  • 01:12:29
    and in 2020 1.5% of us businesses had
  • 01:12:34
    any investment in AI okay perhaps it has
  • 01:12:36
    increased a little bit in the last three
  • 01:12:38
    years but it's still very very much
  • 01:12:41
    embryonic so it's all to play for it's
  • 01:12:43
    going to be the next decade or so where
  • 01:12:45
    we're going to see how much AI what type
  • 01:12:47
    of AI and who's going to benefit from it
  • 01:12:49
    so I think being realistic about where
  • 01:12:52
    we are and what we can do is a very
  • 01:12:54
    important part of it thank you okay and
  • 01:12:58
    that's a really good point to end on so
  • 01:13:00
    I want to say a massive thank you to to
  • 01:13:02
    Darren for joining us and and giving us
  • 01:13:04
    your thoughts and thank you to all of
  • 01:13:05
    you for coming along as well thank you
Tags
  • tehnologie
  • economia
  • automatizare
  • AI
  • inegalitate
  • putere
  • progres economic
  • revoluție industrială
  • istorie
  • inovație